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Feature Engineering for Machine Learning

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Overview

Explore feature engineering using UCI's Abalone Dataset in this course. Enhance your skills in feature extraction, selection, and transformation to boost machine learning model performance. Learn to craft valuable features, apply different selection strategies, and use feature combinations to uncover data patterns.

Syllabus

Unit 1: Exploring Feature Engineering with the UCI Abalone Dataset

Display Dataset Features
Display Dataset Descriptive Features
Display More Dataset Entries

Unit 2: Navigating Practical Challenges in Feature Engineering

Implementing Median Imputation for Numeric Features
Applying Label Encoding to Categorical Data
Debugging the Feature Engineering Pipeline
Applying Categorical Encoding and Median Imputation on the Abalone Dataset

Unit 3: Unlocking the Secrets of Feature Extraction with the Abalone Dataset

Calculating the Area
Volume As a Feature?
What's Their Density?
Calculate Relative Height

Unit 4: Strategies for Effective Feature Selection in Machine Learning

Playing With Wrapper Method
Implementing Embedded Feature Selection Method using Lasso
Refining Feature Selection with f_classif

Unit 5: Harnessing Feature Combinations for Enhanced Machine Learning Models

Create a New Feature by Multiplying Length and Diameter
Debugging Feature Combinations Analysis Code
Exploring New Feature Combinations in Abalone Dataset

Unit 6: Unveiling the Power of Feature Interaction in Machine Learning Model Accuracy

Assessing the Impact of the 'Viscera_Shell' Feature on Model Performance
Alter the Linear Regression Model for a Different Feature Combination
Debugging 'Viscera_Shell' Feature
Effect on Model Performance by Engineering a New Feature

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